Robust fault estimation for wind turbine energy via hybrid systems.

The rapid development of modern wind turbine technology has led to increasing demand for improving system reliability and practical concern for robust fault monitoring scheme. This paper presents the investigation of a 5 MW Dynamic Wind Turbine Energy System that was designed to sustain condition monitoring and fault diagnosis with the goal of improving the reliability operations of universal practical control systems. A hybrid stochastic technique is proposed based on an augmented observer combined with eigenstructure assignment for the parameterisation and the genetic algorithm (GA) optimisation to address the attenuation of uncertainty mostly generated by disturbances. Scenarios-based are employed to explore sensor and actuator faults that have direct and indirect impacts on modern wind turbine system, based on monitoring components that are prone to malfunction. The analysis is aimed to determine the effect of concerned simulated faults from uncertainty in respect to environmental disturbances mostly challenged in real-world operations. The efficiency of the proposed approach will improve the reliability performance of wind turbine system states and diagnose uncertain faults simultaneously. The simulation outcomes illustrate the robustness of the dynamic turbine systems with a diagnostic performance to advance the practical solutions for improving reliable systems.N/

Cost of energy optimisation of offshore wind turbine pmg drive trains based on uncertainty

Offshore wind is a constantly evolving industry as the demand for clean renewable sources of energy continues to grow globally. The cost of energy (COE) is measure of the cost per unit of energy supplied by a provider over the lifetime of a project. Methods in which to reduce this cost are always a priority for parties involved in the design, installation and operation of a wind farm. This thesis explores the contribution of wind turbine drive train design and optimisation in the reduction of COE whilst providing a review of the most sensitive design parameters.Chapters 1 and 2 provide the background to the COE calculation for offshore wind turbines and introduces some of the key issues with current models and challenges for creating reliable designs. Chapter 3 outlines the COE model methodology and introduces base case results for 4 different drive trains with permanent magnet generators (three geared designs and one direct drive topology without a gearbox). Chapter 4 provides an optimisation process based on a genetic algorithm to allow the design to be improved whilst considering several constraints. Chapter 5 looks at the optimised designs under different price input conditions to assess the impact on the COE. Chapter 6 introduces the concepts of robust optimisation and optimisation under uncertainty to account for price variability associated with material used in the drive train.This thesis provides a novel approach to drive train optimisation whilst accounting for price uncertainty. The study highlights the key vulnerabilities for a design under material price fluctuations and presents design processes which include uncertainty and provide robust solutions for various drive train topologies.The effect of drive train design and optimisation suggests that direct drive topologies (that do not have a gearbox) can offer the lowest COE solutions. This is primarily due to the increase in reliability achieved by eradicating the failures associated with wind turbine gearboxes. This result supports current trends observed in large offshore wind turbines where many of the installed >6MW machines are direct drive permanent magnet generators. Additionally, a well-designed drive train has the potential to reduce the COE by up to 15% as discussed in this thesis.Offshore wind is a constantly evolving industry as the demand for clean renewable sources of energy continues to grow globally. The cost of energy (COE) is measure of the cost per unit of energy supplied by a provider over the lifetime of a project. Methods in which to reduce this cost are always a priority for parties involved in the design, installation and operation of a wind farm. This thesis explores the contribution of wind turbine drive train design and optimisation in the reduction of COE whilst providing a review of the most sensitive design parameters.Chapters 1 and 2 provide the background to the COE calculation for offshore wind turbines and introduces some of the key issues with current models and challenges for creating reliable designs. Chapter 3 outlines the COE model methodology and introduces base case results for 4 different drive trains with permanent magnet generators (three geared designs and one direct drive topology without a gearbox). Chapter 4 provides an optimisation process based on a genetic algorithm to allow the design to be improved whilst considering several constraints. Chapter 5 looks at the optimised designs under different price input conditions to assess the impact on the COE. Chapter 6 introduces the concepts of robust optimisation and optimisation under uncertainty to account for price variability associated with material used in the drive train.This thesis provides a novel approach to drive train optimisation whilst accounting for price uncertainty. The study highlights the key vulnerabilities for a design under material price fluctuations and presents design processes which include uncertainty and provide robust solutions for various drive train topologies.The effect of drive train design and optimisation suggests that direct drive topologies (that do not have a gearbox) can offer the lowest COE solutions. This is primarily due to the increase in reliability achieved by eradicating the failures associated with wind turbine gearboxes. This result supports current trends observed in large offshore wind turbines where many of the installed >6MW machines are direct drive permanent magnet generators. Additionally, a well-designed drive train has the potential to reduce the COE by up to 15% as discussed in this thesis

Large Grid-Connected Wind Turbines

This book covers the technological progress and developments of a large-scale wind energy conversion system along with its future trends, with each chapter constituting a contribution by a different leader in the wind energy arena. Recent developments in wind energy conversion systems, system optimization, stability augmentation, power smoothing, and many other fascinating topics are included in this book. Chapters are supported through modeling, control, and simulation analysis. This book contains both technical and review articles

An integrated operation and maintenance framework for offshore renewable energy

Offshore renewable devices hold a large potential as renewable energy sources, but their deployment costs are still too high compared to those of other technologies. Operation and maintenance, as well as management of the assets, are main contributors to the overall costs of the projects, and decision-support tools in this area are required to decrease the final cost of energy.\\ In this thesis a complete characterisation and optimisation framework for the operation, maintenance and assets management of an offshore renewable farm is presented. The methodology uses known approaches, based on Monte Carlo simulation for the characterisation of the key performance indicators of the offshore renewable farm, and genetic algorithms as a search heuristic for the proposal of improved strategies. These methods, coupled in an integrated framework, constitute a novel and valuable tool to support the decision-making process in this area. The methods developed consider multiple aspects for the accurate description of the problem, including considerations on the reliability of the devices and limitations on the offshore operations dictated by the properties of the maintenance assets. Mechanisms and constraints that influence the maintenance procedures are considered and used to determine the optimal strategy. The models are flexible over a range of offshore renewable technologies, and adaptable to different offshore farm sizes and layouts, as well as maintenance assets and configurations of the devices. The approaches presented demonstrate the potential for cost reduction in the operation and maintenance strategy selection, and highlight the importance of computational tools to improve the profitability of a project while ensuring that satisfactory levels of availability and reliability are preserved. Three case studies to show the benefits of application of such methodologies, as well as the validity of their implementation, are provided. Areas for further development are identified, and suggestions to improve the effectiveness of decision-making tools for the assets management of offshore renewable technologies are provided.European CommissionMojo Ocean Dynamics Ltd. T/A Mojo Maritime Lt

Reliability-constrained design optimisation of extra-large offshore wind turbine support structures

The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria.The offshore wind industry has evolved significantly over the last decade, contributing considerably to Europe’s energy mix. For further penetration of this technology, it is essential to reduce its costs to make it competitive with conventional power generation technologies. To this end, optimising the design of components while simultaneously fulfilling design criteria is a crucial requirement for producing more cost-effective strategies. Traditional design optimisation techniques rely on the optimisation of design variables against constraints such as stresses or deformation in the form of limit states and to minimise an objective function such as the total mass of a component. Although this approach leads to more optimal designs, the presence of uncertainties, for instance, in material properties, manufacturing tolerances and environmental loads, requires more systematic consideration of these uncertainties. A combination of optimisation methods with concepts of structural reliability can be a suitable approach if challenges such as the approximation of the load effect concerning global input loads and computational requirements are addressed accordingly. In this study, a reliability-constrained optimisation framework for offshore wind turbine (OWT) support structures is developed, applied, and documented for the first time. First, a parametric finite element analysis (FEA) model of OWT support structures is developed, considering stochastic material properties and environmental loads. The parametric FEA model is then combined with response surface and Monte Carlo (MC) to create an assessment model in the Six Sigma module in ANSYS, which is then further integrated with an optimisation algorithm to develop a fully coupled reliability-constrained optimisation framework. The framework is applied to the NREL 5MW OWT and OC3 sub-structure. Results indicate that the proposed optimisation framework can effectively reduce the mass of OWT support structures meeting target reliability levels focusing on realistic limit states. At the end of the optimisation loop, an LCOE comparison is done to see the effect of mass reduction on the wind turbine cost. The study expanded with a scaling-up approach and investigated the technical feasibility of increasing the system’s power and size in deeper water depth for bottom-fixed support structures. Additionally, parametric equations have been developed to estimate the wind turbine rating and weight considering water depth in the conceptual design stage. Furthermore, the sensitivity analysis was performed on the latest reference support structure of the IEA 15MW turbine to see the effect of water depth between 30m to 60m. The results showed the influences of water depth on the current structural response of the monopile. It revealed that utilising the proposed support structure is not feasible for water-depth above 50m as the analysis did not fulfil design criteria

Recent advances on data-driven services for smart energy systems optimization and pro-active management

Optimization and proactive management of energy systems are crucial for achieving sustainability, efficiency and resilience in future smart energy networks. Data-driven approaches offer promising solutions for tackling the complex and dynamic challenges of energy systems, such as uncertainty, variability, and heterogeneity. Meanwhile, recent advances in decreasing hardware costs and improving data accessibility have allowed for the collection of high-quality data, leading to the development of more accurate and robust data-driven models of different energy systems. In this study, a comprehensive overview of current and future trends in data-driven optimization for smart energy systems is presented. After introducing the motivation and the background of this research field, the potential applications and benefits of optimization in various domains is discussed, such as electric vehicles charge, district heating networks and energy districts. Subsequently this review focuses on different methods and techniques for data-driven optimization and proactive management, ranging from scientific models to machine learning algorithms. Finally, the novel European project, DigiBUILD, is introduced, where different case studies are tested in several pilots, including electric vehicle charging management for increasing renewable energy source consumption, district heating network operative costs optimization and building energy and comfort management

Wind Farm

During the last two decades, increase in electricity demand and environmental concern resulted in fast growth of power production from renewable sources. Wind power is one of the most efficient alternatives. Due to rapid development of wind turbine technology and increasing size of wind farms, wind power plays a significant part in the power production in some countries. However, fundamental differences exist between conventional thermal, hydro, and nuclear generation and wind power, such as different generation systems and the difficulty in controlling the primary movement of a wind turbine, due to the wind and its random fluctuations. These differences are reflected in the specific interaction of wind turbines with the power system. This book addresses a wide variety of issues regarding the integration of wind farms in power systems. The book contains 14 chapters divided into three parts. The first part outlines aspects related to the impact of the wind power generation on the electric system. In the second part, alternatives to mitigate problems of the wind farm integration are presented. Finally, the third part covers issues of modeling and simulation of wind power system

Internationales Kolloquium über Anwendungen der Informatik und Mathematik in Architektur und Bauwesen : 20. bis 22.7. 2015, Bauhaus-Universität Weimar

The 20th International Conference on the Applications of Computer Science and Mathematics in Architecture and Civil Engineering will be held at the Bauhaus University Weimar from 20th till 22nd July 2015. Architects, computer scientists, mathematicians, and engineers from all over the world will meet in Weimar for an interdisciplinary exchange of experiences, to report on their results in research, development and practice and to discuss. The conference covers a broad range of research areas: numerical analysis, function theoretic methods, partial differential equations, continuum mechanics, engineering applications, coupled problems, computer sciences, and related topics. Several plenary lectures in aforementioned areas will take place during the conference. We invite architects, engineers, designers, computer scientists, mathematicians, planners, project managers, and software developers from business, science and research to participate in the conference

Internal Model Control (IMC) design for a stall-regulated variable-speed wind turbine system

A stall-regulated wind turbine with fixed-speed operation provides a configuration which is one of the cheapest and simplest forms of wind generation and configurations. This type of turbine, however, is non-optimal at low winds, stresses the component structure and gives rise to significant power peaks during early stall conditions at high wind speeds. These problems can be overcome by having a properly designed generator speed control. Therefore, to track the maximum power locus curve at low winds, suppress the power peaks at medium winds, limit the power at a rated level at high winds and obtain a satisfactory power-wind speed curve performance (that closely resembles the ideal power-wind speed curve) with minimum stress torque simultaneously over the whole range of the wind speed variations, the availability of active control is vital. The main purpose of this study is to develop an internal model control (IMC) design for the squirrel-cage induction generator (SCIG), coupled with a full-rated power converter of a small (25 kW), stall-regulated, variable-speed wind-turbine (SRVSWT) system, which is subject to variations in the generator speed, electromagnetic torque and rotor flux. The study was done using simulations only. The objective of the controller was to optimise the generator speed to maximise the active power generated during the partial load region and maintain or restrict the generator speed to reduce/control the torque stress and the power-peaking between the partial and full load regions, before power was limited at the rated value of 25 kW at the full load region. The considered investigation involved estimating the proportional-integral (PI) and integral-proportional (IP) controllers parameter values used to track the stator-current producing torque, the rotor flux and the angular mechanical generator speed, before being used in the indirect vector control (IVC) and the sensorless indirect vector control (SLIVC) model algorithms of the SCIG system. The design of the PI and IP controllers was based on the fourth-order model of the SCIG, which is directly coupled to the full-rated power converter through the machine stator, whereas the machine rotor is connected to the turbine rotor via a gearbox. Both step and realistic wind speed profiles were considered. The IMC-based PI and IP controllers (IMC-PI-IP) tuning rule was proven to have smoothened the power curve and shown to give better estimation results compared to the IMC-based PI controllers (IMC-PI), Ziegler-Nichols (ZN) and Tyreus-Luyben (ZN) tuning rules. The findings also showed that for the SRVSWT system that employed the IVC model algorithm with the IMC-PI-IP tuning rule, considering the application of a maintained/constant speed (CS) strategy at the intermediate load region is more profitable than utilizing SRVSWT with the modified power tracking (MoPT) strategy. Besides that, the finding also suggested that, for the IMC-PI-IP approach, the IVC does provide better power tracking performance than the SLIVC model algorithm